Why Sports Are Streaky

If you don’t like what’s happening to your favorite team or player in a sporting event you’re watching, keep watching: Things could turn around in a hurry.

European Pressphoto Agency

Fernando Verdasco was outplayed in the first two sets of his second-round match at the Australian Open, but turned things around to advance.

There have been a few notable turnarounds lately. The Ravens scored 21 straight points against Pittsburgh in their divisional clash on Saturday, taking a 14-point lead, before Pittsburgh responded with 17 unanswered points and ended up winning by a touchdown. The next day, the Bears scored the first 28 points of their game against the Seahawks before Seattle scored 24 of the game’s last 31 points to make things somewhat interesting. And most bizarrely, Pitt scored the first 19 points of its game against undefeated Syracuse on Monday, then saw that lead quickly evaporate to two as the visiting Orange scored 17 straight. The abrupt change was reminiscent of the 2008 national semifinal, when Kansas went up 38-12 over North Carolina — rendering the game effectively over, according to CBS analyst Billy Packer — only to see the lead shrink to four in the second half.

Announcers might call these comebacks swings in momentum. Statistical analysts are more inclined to see them as signs of randomness. Teams’ true abilities aren’t always reflected in a season’s worth of results, let alone a single game, or a passage of play in one game. So Pitt wasn’t really as good as it looked in that initial run, nor as bad as it looked when Syracuse came back.

Consider basketball analyst Ken Pomeroy’s recent experiment with plus/minus stats in college basketball. These stats purport to capture all of a player’s worth by indicating how many points his team outscored opponents by while he was on the court. The problem is, random chance will make the statistic so noisy as to be useless over small sets of games. Pomeroy ran a simulation for a hypothetical player whose plus/minus should be zero. But in some games in the simulation his team was much better off with him on the court, and in others much better without him. “My team is playing a schedule against teams it’s evenly-matched with,” Pomeroy writes of his simulation. “Yet in game 16 we were outscored by 25 points! You can imagine the game-stories written about that one: How my team was out-coached, or how the opponent is just a bad matchup for my team, or how my team quit, showed no heart, etc. But I can assure you, none of that occurred! It was just a bunch of bits and bytes that produced the outcome.”

Pomeroy doesn’t mean that players really are no more than bits and bytes, just that we are all affected by the randomness of events, and that the power of these effects often is underappreciated. Consider the concept of a player on a hot streak in basketball. It was refuted in a 1985 study: A player who hit his last shot is no more likely than usual to hit his next one. In their new book “Scorecasting,” Tobias Moskowitz and L. Jon Wertheim write that more recent studies show that NBA players who seem to have a hot hand are more likely to miss their next shots, because they feel confident and take riskier ones; and that seemingly hot teams tend to be outscored after spurts of strong play.

One sport has produced even more dramatic and unlikely reversals of fortune lately than others: tennis. At the Sydney warm-up event for the Australian Open, Kim Clijsters won the first five games of the final against Li Na, then lost 13 of the last 17 games to drop the match in two sets. In one men’s semifinal, Gilles Simon and Ernests Gulbis were knotted at six games apiece in the first set. Simon won 31 of the next 35 points to take the match. And at the Australian Open so far, comebacks have abounded. Venus Williams dropped the first set in a tiebreak on Wednesday, injuring her groin on set point, but won 12 of 16 games to take the match. Six men already have come back from two sets to none to win three straight sets and the match, including Gael Monfils, who dug out of a 5-2, third-set hole to win 17 of the last 20 games in his first-round match against Thiemo de Bakker. On Wednesday, Fernando Verdasco lost the first two sets to Janko Tipsarevic, then shrugged off three match points late in the fourth set and won 32 of the last 37 points of the match to complete the comeback.

Though complete play-by-play data isn’t readily available to make a more rigorous analysis, it makes sense that tennis would have even more turnarounds than other sports, because of factors other than randomness. Team sports involve multiple players, so any one is unlikely to have an enormous impact on the outcome for a long stretch of play, plus one player on a hot streak could be offset by one on a cold run. Also teams often are playing ones they are very familiar with, in a familiar environment over a relatively fixed, familiar timeframe. Coaches who see a team struggling can stop play and make adjustments. And players suffering from injury, fatigue or lack of motivation can be subbed if they don’t take themselves out or step up their effort for fear of letting down their teammates. Those factors tend to keep games in some sort of equilibrium, though randomness can still help set off a streak or series of them, like in Pitt-Syracuse.

In tennis, though, players are on their own. Underdogs may be fit enough to compete for two sets, but not five — and they can’t win by running out the clock; they have to score the final point of the match. Their opponents may be unfamiliar with them, especially at major tournaments with 128-player draws, and they may have retooled their game since the last meeting — all of which means favorites can get off to slow starts, and in-match adjustments can have big effects, especially because in tennis’s scoring system, a player winning 55% of points can appear to be blowing out his opponents. An injured player can’t call in a replacement, nor get more than a brief break for treatment. And sometimes players simply lose will, as Tipsarevic did, barely waving at Verdasco’s last three serves of the match. Player psychology matters more when players are all alone, with no in-match coaching, in a sport where some players readily admit their mental game is what keeps them from rising higher rankings. All this makes for stunning turnarounds that really aren’t that stunning.

Comments (2 of 2)

I'm glad people are (finally) recognizing the true effects of randomness. I learned about the "hot hand" study in "How We Know What Isn't So" by Thomas Gilovich, a book I recommend to anyone.

At halftime of the BCS national championship game, Urban Meyer commented that it was a "game of momentum" and that Auburn had seized the momentum at one point and Oregon had seized the momentum at another point. In the case of Oregon, the "momentum" arose when there was a long pass to set up a touchdown. Did Meyer bother to ask what happened next? (Answer: a long Auburn drive that was stopped on the Oregon 1, whereupon Oregon took over and was tackled in the end zone for a safety -- which Meyer, of course, called a "momentum shift.") It simply made no sense. But god forbid anyone try to test a hypothesis against the record of what actually happened; that's when we see phrases like "stat geek" and "you've obviously never played the game" tossed around.

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